Various types of industries employ different processes such as continuous, discrete, batch like processes and so on. An industrial process may be of substantially large and/or complex nature. Examples of the processes include, without limiting to these, processes for chemical plants, oil refineries, pharmaceutical or petrochemical industries, food and beverage industries, pulp and paper mills, power plants, steel mills, metals and foundry plants, automated factories and so on.
A process may need to be analysed for various reasons. The results of the analysis may be used e.g. as a support in the control of the process, for producing information that is needed later on e.g. when processing the end product of the process, for diagnostic of events such as a fault or abnormality diagnostic and so on. It is also possible to diagnostic complex products or their parts and/or optimise assets by means of process analysis.
The term ‘event’ shall be understood to refer to any abnormality or failure/fault or any other deviation from normal operation conditions of the process.
A process can be analysed based on input information that has been gathered from various sources associated with the process. The input information may be associated, for example, with various stages of a continuous process, product or asset to be controlled and/or various elements of the processing apparatus and so on. Various alarm and signal information may be used as input information in a process analysis. The available alarm and signal information may be collected from a number of sources such as from different diagnostics packages, a maintenance system, from equipment data and/or knowledge database, various sensors, soft sensors or meters and so on. The input information may also include information which is not obtained directly from the process, such as information regarding the environment in which the process is run. For example, the input information may be associated with the climate conditions surrounding the process, time and so on. The analysing function shall thus be able to analyse different types of information provided by different information sources.
The provision of the input information may be periodic or continuous, depending on the application. The information can be collected automatically or manually. For example, at least a part of the information may be produced and/or fed in manually by a human operator.
Analysis of an industrial process typically includes uncertainties. A reason for this is that the process conditions may vary in time. The process may also experience unexpected events. A process may also pass different transitions between different stages. Although the conditions may be stable in each of the stage, the transitions may introduce changes and variations in the process flow. These variations may be within acceptable limits and thus are not necessarily symptoms of any failures or abnormalities. However, in some instances these changes and/or variations could indeed be caused e.g. by a failure in a part of the process. The uncertainties may exist both in domain knowledge and quantitative information base of the process.
A process diagnostics system should be able to produce substantially accurate information despite the uncertainties. Therefore an industrial process diagnostic system and/or control system is needed that is capable of efficiently handling these uncertainties. More particularly, since the knowledge data and other data may incorporate uncertainties the analysis should be able to deal effectively with probabilities and uncertainties in order to provide appropriate decision support tool for the process operator.
Not all alarm signals produces by alarm information sources associated with the process are necessarily true alarms. Therefore the diagnostic system should also be capable of distinguishing between true and false alarms.
In prior art arrangements the fault indications e.g. in a process plant are connected to an alarm list function or similar record function that is then presented to an operator. For example, a list comprising different types of alarms such as the so called effect alarms and root cause alarms may be presented to the operator. Purely statistical methods such as variance and standard distribution and so on have been used to generate simple fault/no fault information.
The operator may thus be provided with a substantial amount of unprocessed information in a form of a list. The operator may need to trace the real root cause of the problem presented by means of the alarms on the list. The operator may need to perform this in a substantially short period of time. However, the operator may not be able to handle this information properly in the time that is available for him to take a control action after the information has been brought into his attention. The operator may not have any tools for selecting the most probable alarms and for effectively determining the real root cause for the problem.
The inventors have recognised that no proper solution that could take the uncertainties into account has been proposed. The inventors have also found that a more advanced decision support tool could be provided if it could be possible to input information such as different alarm signals for an analyzing function. It might also in some situations be advantageous if the produces decision support information could be ranked based on the probability of causes and/or probability of suitable control actions. Integration of the results of the analysis with the maintenance system and equipment data might also be advantageous in some applications. The integration with the maintenance system and equipment data could be used, for example, for providing an operator with a connection to asset optimisation and prediction of optimal timing e.g. for replacement or repair of a component associated with the process.